Validation of the YLS/CMI on an Australian Juvenile Offending Population

• Author: Kristie Dellar,Lynne Roberts,Jonathan Bullen,Kristy Downe,Robert Kane
• DOI: http://doi.org/10.1177/0306624X221086556
• Published date: 01 June 2023
• Date: 01 June 2023
• Subject Matter: Articles

International Journal of

Offender Therapy and

Comparative Criminology

2023, Vol. 67(8) 861 –883

© The Author(s) 2022

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DOI: 10.1177/0306624X221086556

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Article

Validation of the YLS/CMI

on an Australian Juvenile

Offending Population

Kristie Dellar1,2 , Lynne Roberts1, Jonathan Bullen1,

Kristy Downe2, and Robert Kane1

Abstract

There is a wealth of research that shows juvenile justice systems that utilize

structured and validated assessment tools, such as the YLS/CMI, are far more

effective at reducing rates of recidivism than those who do not. In line with this

research, the Department of Justice (DoJ) in Western Australia adopted the YLS/

CMI as the standard risk assessment tool for evaluating the criminogenic risk and

needs of youth entering the justice system. While there is evidence supporting the

utility of the YLS/CMI in predicting recidivism, there is little research demonstrating

its effectiveness in Australian juvenile populations and no such research in a West

Australian population. There is also a lack of research on the utility of the tool

with young Indigenous offenders, which is particularly concerning given the significant

overrepresentation of Indigenous people in the Australian criminal justice system.

Our study was the first to examine the reliability and predictive validity of the YLS/

CMI on a sample of West Australian juvenile offenders. In this paper, we present the

results of two analyses. The first examines the properties of the YLS/CMI in a cohort

of 4,653 juvenile offenders in Western Australia, including factor structure, internal

consistency, and differences between male and female youth and between Indigenous

and non-Indigenous youth. Consistent with our hypotheses, the tool demonstrated

excellent internal consistency (α = .91) and cross-validation analyses identified

significant differences between groups on total YLS scores and risk domains. The

second analysis examines the predictive validity of the YLS/CMI in a subsample of

921 youth with a minimum follow-up period of 2 years. The overall recidivism rate

was 74.8% and there were differences in scores and recidivism rates for Indigenous

compared to non-Indigenous youth, although the predictive accuracies in terms of

AUC were similar (c. = 0.65 and 0.66, respectively).

1School of Psychology Curtin University, Perth, WA, Australia

2Department of Justice, Perth, WA, Australia

Corresponding Author:

Kristie Dellar, 100 Aurum Parade, Dayton, WA 6055, Australia.

Email: kristie.dellar@postgrad.curtin.edu.au

1086556IJOXXX10.1177/0306624X221086556International Journal of Offender Therapy and Comparative CriminologyDellar et al.

research-article2022

862 International Journal of Offender Therapy and Comparative Criminology 67(8)

Keywords

YLS/CMI, predictive validity, recidivism, risk assessment, Indigenous youth

The age-crime curve posits that adolescents and young adults are more likely than any

other age group to engage in offending and delinquent behavior (Farrington et al.,

2008; Viljoen et al., 2016). A key task for the juvenile justice system is to classify

young offenders based on their level of risk of reoffending in order to make important

decisions regarding the type and level of intervention required (Hoge, 2012).

Assessments that focus on evaluation of risk of reoffending (i.e., criminogenic risk)

may also contribute to decisions on appropriate disposition following a conviction, for

example, deciding whether the young person should receive custody or community

supervision. Assessments that focus on criminogenic needs help to identify individual

risk factors that are changeable through interventions, such as antisocial attitudes, in

order to reduce the likelihood of future reoffending. Needs assessments are pivotal in

regards to the management of risk and where interventions are included in the judicial

action (Hoge, 2012). Consequently, the safety of the community is enhanced whereby

a reduction in reoffending means there are less people who are victimized.

There are four generations identified in the development of risk assessment mea-

sures. First generation risk assessments are referred to as unstructured clinical judge-

ment assessments, and were based on the unstructured judgement of a professional and

their knowledge and experience (Davis & Ogloff, 2008; Schwalbe, 2007). Research

has shown that unstructured clinical judgements are relatively poor at predicting recid-

ivism, with an accuracy rate of approximately one out of three (Davis & Ogloff, 2008;

Hart, 2003; Monahan, 1981). Second generation instruments, known as actuarial risk

assessments, comprised mainly of static (unchangeable) risk factors, such as criminal

history or age at first offence (Davis & Ogloff, 2008). Whilst actuarial risk assess-

ments have been shown to predict the risk of reoffending with moderate levels of

accuracy, they do not provide information on risk reduction strategies (Davis & Ogloff,

2008; van der Put et al., 2014). Third generation instruments are those that include

static as well as dynamic (changeable) risk factors, thereby providing insight into

potential strategies for managing the risk of recidivism (Davis & Ogloff, 2008).

Finally, fourth generation risk assessments are those that include protective factors, in

addition to static and dynamic risk factors, and they provide a link between those fac-

tors and case management (Davis & Ogloff, 2008; van der Put et al., 2014). Research

has continued to demonstrate that structured risk assessments (i.e., second, third, and

fourth generation assessments), outperform clinical assessment in terms of predicting

recidivism (Hoge, 2012).

Recent advances in the development of psychometric risk assessment tools have

encouraged a standardized approach to evaluating risk factors for antisocial behavior.

Risk assessment measures were designed to improve clinical judgment and decision

making in predicting recidivism (Hoge, 2012; Lennings, 2008). Initially, research on

adolescent risk assessment lagged behind the adult risk assessment field, with the first

adolescent risk assessment measures developed 5 to 10 years after the first adult risk